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willyesiam/Qwen38Jrell-MTP-256K
Qwen38Jrell-MTP-256K is a machine learning model from willyesiam. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for gguf. The card lists the license as apache-2.0.
This repository is an Ollama deployment package for Qwen3.8 27B. It combines the jrell IQ4XS-style main quantization with a separate Q40 MTP draft model and targets a 262,144-token (256K) context window.
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From the Hugging Face model README
This repository is an Ollama deployment package for Qwen3.8 27B. It combines the jrell IQ4_XS-style main quantization with a separate Q4_0 MTP draft model and targets a 262,144-token (256K) context window.
This is a packaging and runtime-configuration project, not a new training run, fine-tune, merge, or official Qwen release. It does not claim ownership of the upstream model or quantizations.
The repository contains scripts, configuration, attribution, and documentation. It does
not contain the large GGUF weights. install.ps1 downloads those artifacts from their
canonical upstream repositories at install time, so this repository can be renamed and
updated without re-uploading multi-GB files.
The current repository contains:
| File | Purpose |
|---|---|
install.ps1 | Downloads the two upstream GGUF files and creates the Ollama model. |
run.ps1 | Runs qwen38-jrell-mtp-256k. |
verify.ps1 | Prints Ollama, process, GPU, and local-file diagnostics. |
Modelfile.template | Exact local-path Modelfile structure used after installation. |
ATTRIBUTION.md | Upstream sources, credits, and licensing notes. |
SHA256SUMS.txt | Instructions for hashing locally downloaded artifacts. |
.gitattributes | Git LFS rules for any large binaries added in the future. |
No .gguf file is stored in this repository. The filenames used by the installer are:
jrell/Qwen3.8-27B-i1-IQ4_XS-GGUF-Smaller:
Qwen3.8-27B-i1-IQ4_XS-GGUF-Smaller.ggufunsloth/Qwen3.8-27B-GGUF:
MTP/mtp-Qwen3.8-27B-Q4_0.ggufThe draft file is stored locally as mtp-Qwen3.8-27B-Q4_0.gguf under
models/mtp/ by the installer.
On Windows, with Ollama on PATH:
powershell -ExecutionPolicy Bypass -File .\install.ps1
ollama run qwen38-jrell-mtp-256k
The installer skips non-empty local GGUF files, generates Modelfile.generated, runs
ollama create, and calls ollama show for validation. It does not alter Ollama's
internal blob store or set permanent environment variables.
The generated Modelfile uses the actual filenames downloaded by the installer:
FROM ./models/main/Qwen3.8-27B-i1-IQ4_XS-GGUF-Smaller.gguf
DRAFT ./models/mtp/mtp-Qwen3.8-27B-Q4_0.gguf
PARAMETER num_ctx 262144
PARAMETER draft_num_predict 2
The paths are relative to the Modelfile. Do not replace them with Ollama's internal blob SHA paths.
Qwen3.8-27B-i1-IQ4_XS-GGUF-Smaller.gguf IQ4_XS-style quantization.MTP/mtp-Qwen3.8-27B-Q4_0.gguf Q4_0 MTP draft.num_ctx 262144.draft_num_predict 2.qwen38-jrell-mtp-256k.draft_num_predict controls the number of speculative draft tokens proposed per step when
a draft model is available. Actual acceleration depends on the installed Ollama version,
hardware, context length, quantization, and workload.
Runtime context:
num_predict = 512.| Configuration | Throughput | 512-token time | Token latency |
|---|---|---|---|
| MTP OFF | 41.02 tok/s | 12.48 s | 24.38 ms/token |
| MTP ON | 54.62 tok/s | 9.37 s | 18.31 ms/token |
Observed throughput speedup: approximately 1.33x.
Observed tok/s improvement: approximately 33.2%.
Observed generation-time reduction in this test: approximately 24.9%.
This is a single-run matched 512-token A/B test, not a statistically rigorous benchmark. On this local Ollama configuration, the run measured 54.62 tok/s with MTP versus 41.02 tok/s without MTP. Results depend on hardware, Ollama version, context length, quantization, runtime configuration, and workload; the observed ratio is not a universal performance claim.
No MTP acceptance-rate data or multi-run median/mean is reported here. The earlier rough "effective TOPS" proxy is intentionally omitted: it was calculated from parameter count and tok/s, not measured GPU hardware TOPS.
The upstream repositories remain the source of the model artifacts. This repository does not claim to have created or trained Qwen3.8.
The original scripts and documentation in this repository are Apache-2.0 licensed; see
LICENSE. The Qwen and Unsloth upstream repositories identify Apache-2.0
licensing in their metadata. The jrell repository does not explicitly state an independent
license in its model-card metadata, so its main GGUF is downloaded from upstream and is not
redistributed here. See ATTRIBUTION.md for detailed credits and terms.
This is not an official Qwen release and is not affiliated with Qwen, Alibaba Cloud, Unsloth, or jrell. The maintainer receives no revenue from this repository.
-NumCtx in install.ps1 if the local system
cannot load the full context.-NumCtx and -DraftNumPredict overrides.SHA256SUMS.txt if you need a
reproducibility record.